Multi-view Attribute Weighted Naive Bayes

Abstract

Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy. Numerous enhancements have been proposed to weaken its attribute conditional independence assumption. However, all of them only focus on the raw attribute view, which is hard to reflect all the data characteristics in real-world applications. To portray data characteristics more comprehensively, in this study, we construct two label views from the raw attributes and propose a novel model called multi-view attribute weighted naive Bayes (MAWNB). In MAWNB, we first build multiple super-parent one-dependence estimators (SPODEs) as well as random trees (RTs), then we utilize each of them to classify each training instance in turn and use all their predicted class labels to construct two label views. Next, to avoid attribute redundancy, we optimize the weight of each attribute value for each class by minimizing the negative conditional log-likelihood (CLL) in each view. Finally, the estimated class-membership probabilities by three views are fused to predict the class label for each test instance. Extensive experiments show that MAWNB significantly outperforms NB and all the other existing state-of-the-art competitors.